Overview

Dataset statistics

Number of variables19
Number of observations33332
Missing cells19216
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory160.0 B

Variable types

Numeric14
Categorical5

Alerts

gcs_eye_opening is highly overall correlated with gcs_motor_response and 1 other fieldsHigh correlation
gcs_motor_response is highly overall correlated with gcs_eye_opening and 1 other fieldsHigh correlation
gcs_verbal_response is highly overall correlated with gcs_eye_opening and 1 other fieldsHigh correlation
gender is highly overall correlated with height_cmHigh correlation
height_cm is highly overall correlated with genderHigh correlation
non_invasive_blood_pressure_diastolic is highly overall correlated with non_invasive_blood_pressure_meanHigh correlation
non_invasive_blood_pressure_mean is highly overall correlated with non_invasive_blood_pressure_diastolicHigh correlation
ethnicity is highly imbalanced (58.0%)Imbalance
pH_dipstick has 18615 (55.8%) missing valuesMissing

Reproduction

Analysis started2023-12-18 20:21:03.873954
Analysis finished2023-12-18 20:21:54.498425
Duration50.62 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct73
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.636715
Minimum18
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:54.638635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile33
Q154
median65
Q375
95-th percentile87
Maximum91
Range73
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.854195
Coefficient of variation (CV)0.24913597
Kurtosis-0.082222898
Mean63.636715
Median Absolute Deviation (MAD)11
Skewness-0.53837329
Sum2121139
Variance251.35549
MonotonicityNot monotonic
2023-12-19T01:51:54.844327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 919
 
2.8%
67 899
 
2.7%
68 885
 
2.7%
66 884
 
2.7%
63 869
 
2.6%
62 863
 
2.6%
70 856
 
2.6%
64 846
 
2.5%
71 813
 
2.4%
65 811
 
2.4%
Other values (63) 24687
74.1%
ValueCountFrequency (%)
18 36
 
0.1%
19 56
0.2%
20 100
0.3%
21 91
0.3%
22 93
0.3%
23 129
0.4%
24 103
0.3%
25 107
0.3%
26 107
0.3%
27 130
0.4%
ValueCountFrequency (%)
91 919
2.8%
89 197
 
0.6%
88 315
 
0.9%
87 375
1.1%
86 390
1.2%
85 483
1.4%
84 531
1.6%
83 539
1.6%
82 576
1.7%
81 592
1.8%

gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size520.8 KiB
M
19913 
F
13419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33332
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 19913
59.7%
F 13419
40.3%

Length

2023-12-19T01:51:55.038247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-19T01:51:55.223260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
m 19913
59.7%
f 13419
40.3%

Most occurring characters

ValueCountFrequency (%)
M 19913
59.7%
F 13419
40.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33332
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 19913
59.7%
F 13419
40.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 33332
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 19913
59.7%
F 13419
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 19913
59.7%
F 13419
40.3%

ethnicity
Categorical

IMBALANCE 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size520.8 KiB
WHITE
21948 
UNKNOWN
3288 
BLACK/AFRICAN AMERICAN
2688 
OTHER
 
991
WHITE - OTHER EUROPEAN
 
583
Other values (28)
3834 

Length

Max length41
Median length5
Mean length8.5303912
Min length5

Characters and Unicode

Total characters284335
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWHITE
2nd rowWHITE
3rd rowWHITE
4th rowWHITE
5th rowBLACK/AFRICAN AMERICAN

Common Values

ValueCountFrequency (%)
WHITE 21948
65.8%
UNKNOWN 3288
 
9.9%
BLACK/AFRICAN AMERICAN 2688
 
8.1%
OTHER 991
 
3.0%
WHITE - OTHER EUROPEAN 583
 
1.7%
UNABLE TO OBTAIN 463
 
1.4%
HISPANIC/LATINO - PUERTO RICAN 363
 
1.1%
ASIAN 326
 
1.0%
HISPANIC OR LATINO 310
 
0.9%
ASIAN - CHINESE 265
 
0.8%
Other values (23) 2107
 
6.3%

Length

2023-12-19T01:51:55.368703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white 22926
50.9%
unknown 3288
 
7.3%
black/african 2819
 
6.3%
american 2809
 
6.2%
2275
 
5.1%
other 1626
 
3.6%
asian 1009
 
2.2%
hispanic/latino 812
 
1.8%
to 705
 
1.6%
european 657
 
1.5%
Other values (38) 6088
 
13.5%

Most occurring characters

ValueCountFrequency (%)
I 36442
12.8%
E 32667
11.5%
T 28448
10.0%
W 26508
9.3%
H 26191
9.2%
A 23996
8.4%
N 23853
8.4%
11682
 
4.1%
C 11647
 
4.1%
R 10309
 
3.6%
Other values (17) 52592
18.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 266296
93.7%
Space Separator 11682
 
4.1%
Other Punctuation 4082
 
1.4%
Dash Punctuation 2275
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 36442
13.7%
E 32667
12.3%
T 28448
10.7%
W 26508
10.0%
H 26191
9.8%
A 23996
9.0%
N 23853
9.0%
C 11647
 
4.4%
R 10309
 
3.9%
O 9201
 
3.5%
Other values (14) 37034
13.9%
Space Separator
ValueCountFrequency (%)
11682
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4082
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2275
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 266296
93.7%
Common 18039
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 36442
13.7%
E 32667
12.3%
T 28448
10.7%
W 26508
10.0%
H 26191
9.8%
A 23996
9.0%
N 23853
9.0%
C 11647
 
4.4%
R 10309
 
3.9%
O 9201
 
3.5%
Other values (14) 37034
13.9%
Common
ValueCountFrequency (%)
11682
64.8%
/ 4082
 
22.6%
- 2275
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 36442
12.8%
E 32667
11.5%
T 28448
10.0%
W 26508
9.3%
H 26191
9.2%
A 23996
8.4%
N 23853
8.4%
11682
 
4.1%
C 11647
 
4.1%
R 10309
 
3.6%
Other values (17) 52592
18.5%

admission_type
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size520.8 KiB
EW EMER.
15506 
URGENT
6976 
SURGICAL SAME DAY ADMISSION
4016 
OBSERVATION ADMIT
3719 
ELECTIVE
1590 
Other values (4)
 
1525

Length

Max length27
Median length8
Mean length11.074313
Min length6

Characters and Unicode

Total characters369129
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEW EMER.
2nd rowEW EMER.
3rd rowEW EMER.
4th rowEW EMER.
5th rowEW EMER.

Common Values

ValueCountFrequency (%)
EW EMER. 15506
46.5%
URGENT 6976
20.9%
SURGICAL SAME DAY ADMISSION 4016
 
12.0%
OBSERVATION ADMIT 3719
 
11.2%
ELECTIVE 1590
 
4.8%
DIRECT EMER. 1378
 
4.1%
EU OBSERVATION 91
 
0.3%
DIRECT OBSERVATION 48
 
0.1%
AMBULATORY OBSERVATION 8
 
< 0.1%

Length

2023-12-19T01:51:55.563523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-19T01:51:55.774049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
emer 16884
25.5%
ew 15506
23.4%
urgent 6976
10.5%
surgical 4016
 
6.1%
same 4016
 
6.1%
day 4016
 
6.1%
admission 4016
 
6.1%
observation 3866
 
5.8%
admit 3719
 
5.6%
elective 1590
 
2.4%
Other values (3) 1525
 
2.3%

Most occurring characters

ValueCountFrequency (%)
E 70419
19.1%
R 33176
 
9.0%
32798
 
8.9%
M 28643
 
7.8%
A 23665
 
6.4%
I 22649
 
6.1%
S 19930
 
5.4%
T 17585
 
4.8%
. 16884
 
4.6%
W 15506
 
4.2%
Other values (10) 87874
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 319447
86.5%
Space Separator 32798
 
8.9%
Other Punctuation 16884
 
4.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 70419
22.0%
R 33176
10.4%
M 28643
9.0%
A 23665
 
7.4%
I 22649
 
7.1%
S 19930
 
6.2%
T 17585
 
5.5%
W 15506
 
4.9%
N 14858
 
4.7%
D 13177
 
4.1%
Other values (8) 59839
18.7%
Space Separator
ValueCountFrequency (%)
32798
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 319447
86.5%
Common 49682
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 70419
22.0%
R 33176
10.4%
M 28643
9.0%
A 23665
 
7.4%
I 22649
 
7.1%
S 19930
 
6.2%
T 17585
 
5.5%
W 15506
 
4.9%
N 14858
 
4.7%
D 13177
 
4.1%
Other values (8) 59839
18.7%
Common
ValueCountFrequency (%)
32798
66.0%
. 16884
34.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 369129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 70419
19.1%
R 33176
 
9.0%
32798
 
8.9%
M 28643
 
7.8%
A 23665
 
6.4%
I 22649
 
6.1%
S 19930
 
5.4%
T 17585
 
4.8%
. 16884
 
4.6%
W 15506
 
4.2%
Other values (10) 87874
23.8%

first_careunit
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size520.8 KiB
Cardiac Vascular Intensive Care Unit (CVICU)
9467 
Medical Intensive Care Unit (MICU)
6141 
Medical/Surgical Intensive Care Unit (MICU/SICU)
4596 
Coronary Care Unit (CCU)
4533 
Surgical Intensive Care Unit (SICU)
3680 
Other values (4)
4915 

Length

Max length48
Median length44
Mean length35.876785
Min length14

Characters and Unicode

Total characters1195845
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrauma SICU (TSICU)
2nd rowCoronary Care Unit (CCU)
3rd rowCoronary Care Unit (CCU)
4th rowCardiac Vascular Intensive Care Unit (CVICU)
5th rowMedical/Surgical Intensive Care Unit (MICU/SICU)

Common Values

ValueCountFrequency (%)
Cardiac Vascular Intensive Care Unit (CVICU) 9467
28.4%
Medical Intensive Care Unit (MICU) 6141
18.4%
Medical/Surgical Intensive Care Unit (MICU/SICU) 4596
13.8%
Coronary Care Unit (CCU) 4533
13.6%
Surgical Intensive Care Unit (SICU) 3680
 
11.0%
Trauma SICU (TSICU) 3573
 
10.7%
Neuro Surgical Intensive Care Unit (Neuro SICU) 724
 
2.2%
Neuro Intermediate 434
 
1.3%
Neuro Stepdown 184
 
0.6%

Length

2023-12-19T01:51:55.953465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-19T01:51:56.143260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
care 29141
17.8%
unit 29141
17.8%
intensive 24608
15.0%
cardiac 9467
 
5.8%
vascular 9467
 
5.8%
cvicu 9467
 
5.8%
sicu 7977
 
4.9%
medical 6141
 
3.7%
micu 6141
 
3.7%
micu/sicu 4596
 
2.8%
Other values (9) 27896
17.0%

Most occurring characters

ValueCountFrequency (%)
130710
 
10.9%
a 98859
 
8.3%
C 98024
 
8.2%
e 92646
 
7.7%
n 83508
 
7.0%
i 83387
 
7.0%
r 72214
 
6.0%
U 70024
 
5.9%
I 61392
 
5.1%
t 54801
 
4.6%
Other values (20) 350280
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 686125
57.4%
Uppercase Letter 304390
25.5%
Space Separator 130710
 
10.9%
Close Punctuation 32714
 
2.7%
Open Punctuation 32714
 
2.7%
Other Punctuation 9192
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 98859
14.4%
e 92646
13.5%
n 83508
12.2%
i 83387
12.2%
r 72214
10.5%
t 54801
8.0%
c 38671
 
5.6%
s 34075
 
5.0%
l 29204
 
4.3%
v 24608
 
3.6%
Other values (8) 74152
10.8%
Uppercase Letter
ValueCountFrequency (%)
C 98024
32.2%
U 70024
23.0%
I 61392
20.2%
S 25330
 
8.3%
M 21474
 
7.1%
V 18934
 
6.2%
T 7146
 
2.3%
N 2066
 
0.7%
Space Separator
ValueCountFrequency (%)
130710
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32714
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32714
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 9192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 990515
82.8%
Common 205330
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 98859
10.0%
C 98024
9.9%
e 92646
 
9.4%
n 83508
 
8.4%
i 83387
 
8.4%
r 72214
 
7.3%
U 70024
 
7.1%
I 61392
 
6.2%
t 54801
 
5.5%
c 38671
 
3.9%
Other values (16) 236989
23.9%
Common
ValueCountFrequency (%)
130710
63.7%
) 32714
 
15.9%
( 32714
 
15.9%
/ 9192
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1195845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130710
 
10.9%
a 98859
 
8.3%
C 98024
 
8.2%
e 92646
 
7.7%
n 83508
 
7.0%
i 83387
 
7.0%
r 72214
 
6.0%
U 70024
 
5.9%
I 61392
 
5.1%
t 54801
 
4.6%
Other values (20) 350280
29.3%

icu_los
Real number (ℝ)

Distinct32351
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0974458
Minimum0.001550926
Maximum133.66728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:56.348433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.001550926
5-th percentile0.74658391
Q11.3586429
median2.7515451
Q35.843423
95-th percentile17.698807
Maximum133.66728
Range133.66573
Interquartile range (IQR)4.4847801

Descriptive statistics

Standard deviation6.841475
Coefficient of variation (CV)1.3421378
Kurtosis31.569251
Mean5.0974458
Median Absolute Deviation (MAD)1.599838
Skewness4.2352979
Sum169908.06
Variance46.805779
MonotonicityNot monotonic
2023-12-19T01:51:56.523527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.118472222 4
 
< 0.1%
1.099039352 4
 
< 0.1%
1.312164352 3
 
< 0.1%
1.187025463 3
 
< 0.1%
0.61849537 3
 
< 0.1%
1.916782407 3
 
< 0.1%
1.278356481 3
 
< 0.1%
1.869895833 3
 
< 0.1%
1.090844907 3
 
< 0.1%
1.386516204 3
 
< 0.1%
Other values (32341) 33300
99.9%
ValueCountFrequency (%)
0.001550926 1
< 0.1%
0.020787037 1
< 0.1%
0.027951389 1
< 0.1%
0.039814815 1
< 0.1%
0.044965278 1
< 0.1%
0.051099537 1
< 0.1%
0.051747685 1
< 0.1%
0.054085648 1
< 0.1%
0.057002315 1
< 0.1%
0.057974537 1
< 0.1%
ValueCountFrequency (%)
133.6672801 1
< 0.1%
124.6729398 1
< 0.1%
110.2322801 1
< 0.1%
106.3720949 1
< 0.1%
101.7262384 1
< 0.1%
99.63844907 1
< 0.1%
95.83821759 1
< 0.1%
91.01376157 1
< 0.1%
88.04150463 1
< 0.1%
86.84840278 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size520.8 KiB
0
29314 
1
4018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33332
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 29314
87.9%
1 4018
 
12.1%

Length

2023-12-19T01:51:56.718629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-19T01:51:56.863435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29314
87.9%
1 4018
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 29314
87.9%
1 4018
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33332
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29314
87.9%
1 4018
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 33332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29314
87.9%
1 4018
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29314
87.9%
1 4018
 
12.1%

heart_rate_mean
Real number (ℝ)

Distinct26234
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.787242
Minimum28.727273
Maximum484.63256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:57.006280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum28.727273
5-th percentile64.331148
Q175.567147
median83.915174
Q393.371111
95-th percentile107.76271
Maximum484.63256
Range455.90529
Interquartile range (IQR)17.803964

Descriptive statistics

Standard deviation13.523472
Coefficient of variation (CV)0.1594989
Kurtosis24.090955
Mean84.787242
Median Absolute Deviation (MAD)8.8400978
Skewness1.1404128
Sum2826128.3
Variance182.88429
MonotonicityNot monotonic
2023-12-19T01:51:57.193365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 30
 
0.1%
83 30
 
0.1%
78 28
 
0.1%
74 27
 
0.1%
88 24
 
0.1%
86 22
 
0.1%
84 21
 
0.1%
73 21
 
0.1%
85 21
 
0.1%
76 19
 
0.1%
Other values (26224) 33089
99.3%
ValueCountFrequency (%)
28.72727273 1
< 0.1%
33.64705882 1
< 0.1%
34.71428571 1
< 0.1%
34.84615385 1
< 0.1%
36.70454545 1
< 0.1%
36.84 1
< 0.1%
37.33333333 1
< 0.1%
39.3 1
< 0.1%
40.12 1
< 0.1%
40.75 1
< 0.1%
ValueCountFrequency (%)
484.6325581 1
< 0.1%
269.9160448 1
< 0.1%
187.5289331 1
< 0.1%
150.0096154 1
< 0.1%
143.3550725 1
< 0.1%
141.4558824 1
< 0.1%
141.0769231 1
< 0.1%
139.8 1
< 0.1%
139.2325581 1
< 0.1%
139 1
< 0.1%

gcs_eye_opening
Real number (ℝ)

HIGH CORRELATION 

Distinct4750
Distinct (%)14.3%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.4143986
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:57.384120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1428571
Q13.16
median3.56983
Q33.9255113
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)0.76551133

Descriptive statistics

Standard deviation0.63138481
Coefficient of variation (CV)0.18491831
Kurtosis3.1134585
Mean3.4143986
Median Absolute Deviation (MAD)0.3784459
Skewness-1.6576932
Sum113767.76
Variance0.39864678
MonotonicityNot monotonic
2023-12-19T01:51:57.575864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 7206
 
21.6%
3 915
 
2.7%
3.5 857
 
2.6%
3.333333333 509
 
1.5%
3.666666667 483
 
1.4%
3.4 424
 
1.3%
3.25 405
 
1.2%
1 381
 
1.1%
3.75 371
 
1.1%
3.2 364
 
1.1%
Other values (4740) 21405
64.2%
ValueCountFrequency (%)
1 381
1.1%
1.006072874 1
 
< 0.1%
1.008 1
 
< 0.1%
1.008130081 1
 
< 0.1%
1.012048193 1
 
< 0.1%
1.013274336 1
 
< 0.1%
1.013793103 1
 
< 0.1%
1.014285714 1
 
< 0.1%
1.015625 2
 
< 0.1%
1.016304348 1
 
< 0.1%
ValueCountFrequency (%)
4 7206
21.6%
3.991666667 1
 
< 0.1%
3.991525424 2
 
< 0.1%
3.991304348 1
 
< 0.1%
3.99122807 1
 
< 0.1%
3.990990991 1
 
< 0.1%
3.989361702 1
 
< 0.1%
3.988372093 1
 
< 0.1%
3.988023952 1
 
< 0.1%
3.987654321 1
 
< 0.1%

gcs_verbal_response
Real number (ℝ)

HIGH CORRELATION 

Distinct5146
Distinct (%)15.4%
Missing11
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.603425
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:57.743350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.6
median3.9333333
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation1.3187393
Coefficient of variation (CV)0.36596829
Kurtosis-0.93355061
Mean3.603425
Median Absolute Deviation (MAD)1.0666667
Skewness-0.59508093
Sum120069.72
Variance1.7390733
MonotonicityNot monotonic
2023-12-19T01:51:57.923774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 8420
25.3%
1 1734
 
5.2%
4 886
 
2.7%
3.666666667 756
 
2.3%
3 670
 
2.0%
4.2 555
 
1.7%
3.4 479
 
1.4%
4.333333333 444
 
1.3%
3.857142857 393
 
1.2%
4.428571429 298
 
0.9%
Other values (5136) 18686
56.1%
ValueCountFrequency (%)
1 1734
5.2%
1.002358491 1
 
< 0.1%
1.003322259 1
 
< 0.1%
1.005464481 1
 
< 0.1%
1.005617978 1
 
< 0.1%
1.005952381 1
 
< 0.1%
1.006535948 2
 
< 0.1%
1.006711409 1
 
< 0.1%
1.007246377 1
 
< 0.1%
1.008097166 1
 
< 0.1%
ValueCountFrequency (%)
5 8420
25.3%
4.995073892 1
 
< 0.1%
4.993006993 1
 
< 0.1%
4.992753623 2
 
< 0.1%
4.99 1
 
< 0.1%
4.989130435 1
 
< 0.1%
4.989010989 1
 
< 0.1%
4.988929889 1
 
< 0.1%
4.987654321 1
 
< 0.1%
4.987012987 5
 
< 0.1%

gcs_motor_response
Real number (ℝ)

HIGH CORRELATION 

Distinct4897
Distinct (%)14.7%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.3775456
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:58.133673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5454545
Q15.125
median5.7184623
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0.875

Descriptive statistics

Standard deviation0.90982566
Coefficient of variation (CV)0.16918976
Kurtosis7.0964
Mean5.3775456
Median Absolute Deviation (MAD)0.28153771
Skewness-2.4447198
Sum179169.07
Variance0.82778274
MonotonicityNot monotonic
2023-12-19T01:51:58.293516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 11691
35.1%
5 1036
 
3.1%
5.166666667 659
 
2.0%
5.285714286 533
 
1.6%
5.5 430
 
1.3%
4.75 402
 
1.2%
5.375 333
 
1.0%
5.666666667 278
 
0.8%
1 253
 
0.8%
5.444444444 227
 
0.7%
Other values (4887) 17476
52.4%
ValueCountFrequency (%)
1 253
0.8%
1.009803922 1
 
< 0.1%
1.014925373 1
 
< 0.1%
1.018691589 1
 
< 0.1%
1.024390244 1
 
< 0.1%
1.026666667 2
 
< 0.1%
1.027777778 1
 
< 0.1%
1.035714286 1
 
< 0.1%
1.042016807 2
 
< 0.1%
1.045454545 2
 
< 0.1%
ValueCountFrequency (%)
6 11691
35.1%
5.99751861 1
 
< 0.1%
5.996309963 1
 
< 0.1%
5.996240602 1
 
< 0.1%
5.996015936 1
 
< 0.1%
5.995934959 1
 
< 0.1%
5.995454545 1
 
< 0.1%
5.99543379 1
 
< 0.1%
5.994818653 1
 
< 0.1%
5.994565217 1
 
< 0.1%

non_invasive_blood_pressure_diastolic
Real number (ℝ)

HIGH CORRELATION 

Distinct20602
Distinct (%)62.3%
Missing276
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean64.166881
Minimum18
Maximum195.35943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:58.483485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile48.425595
Q156.964286
median63.3
Q370.396343
95-th percentile82.633862
Maximum195.35943
Range177.35943
Interquartile range (IQR)13.432058

Descriptive statistics

Standard deviation10.794748
Coefficient of variation (CV)0.16822927
Kurtosis6.0019306
Mean64.166881
Median Absolute Deviation (MAD)6.7231664
Skewness1.0028743
Sum2121100.4
Variance116.52658
MonotonicityNot monotonic
2023-12-19T01:51:58.657842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 76
 
0.2%
65 75
 
0.2%
61 75
 
0.2%
62 71
 
0.2%
64 69
 
0.2%
58 68
 
0.2%
57 63
 
0.2%
54 60
 
0.2%
56 60
 
0.2%
67 59
 
0.2%
Other values (20592) 32380
97.1%
(Missing) 276
 
0.8%
ValueCountFrequency (%)
18 1
< 0.1%
21.9375 1
< 0.1%
23 1
< 0.1%
26 1
< 0.1%
26.28571429 1
< 0.1%
27 1
< 0.1%
31.28571429 1
< 0.1%
31.3030303 1
< 0.1%
31.5 1
< 0.1%
32 1
< 0.1%
ValueCountFrequency (%)
195.3594306 1
< 0.1%
194.1052632 1
< 0.1%
186.5319149 1
< 0.1%
183.9365079 1
< 0.1%
177.619256 1
< 0.1%
174.0344828 1
< 0.1%
170.98 1
< 0.1%
169.2352941 2
< 0.1%
168.6103896 1
< 0.1%
165.5714286 1
< 0.1%

non_invasive_blood_pressure_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct20426
Distinct (%)61.8%
Missing288
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean77.2587
Minimum24.666667
Maximum195.2766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:58.890772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum24.666667
5-th percentile62.100306
Q169.849283
median76.199291
Q383.560351
95-th percentile96
Maximum195.2766
Range170.60993
Interquartile range (IQR)13.711067

Descriptive statistics

Standard deviation10.544212
Coefficient of variation (CV)0.13647928
Kurtosis1.7005129
Mean77.2587
Median Absolute Deviation (MAD)6.7764659
Skewness0.66729107
Sum2552936.5
Variance111.18041
MonotonicityNot monotonic
2023-12-19T01:51:59.063261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 74
 
0.2%
77 72
 
0.2%
73 70
 
0.2%
78 66
 
0.2%
74 66
 
0.2%
70 66
 
0.2%
79 65
 
0.2%
71 63
 
0.2%
75 61
 
0.2%
81 61
 
0.2%
Other values (20416) 32380
97.1%
(Missing) 288
 
0.9%
ValueCountFrequency (%)
24.66666667 1
 
< 0.1%
30.9375 1
 
< 0.1%
31 1
 
< 0.1%
38.54545455 1
 
< 0.1%
39.42857143 1
 
< 0.1%
41 1
 
< 0.1%
41.30331754 2
< 0.1%
43 3
< 0.1%
44 1
 
< 0.1%
44.22222222 1
 
< 0.1%
ValueCountFrequency (%)
195.2765957 1
< 0.1%
169.6428571 1
< 0.1%
162.2926829 1
< 0.1%
158.4285714 1
< 0.1%
152.3875 2
< 0.1%
152.2207792 1
< 0.1%
141.5420561 1
< 0.1%
140.9541284 2
< 0.1%
131.9565217 1
< 0.1%
130.4493671 1
< 0.1%

respiratory_rate
Real number (ℝ)

Distinct20718
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.339948
Minimum8.3958333
Maximum63.839506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:59.278574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum8.3958333
5-th percentile14.541495
Q116.980344
median19
Q321.354914
95-th percentile25.23969
Maximum63.839506
Range55.443673
Interquartile range (IQR)4.3745705

Descriptive statistics

Standard deviation3.338193
Coefficient of variation (CV)0.1726061
Kurtosis2.3387551
Mean19.339948
Median Absolute Deviation (MAD)2.1715922
Skewness0.74349696
Sum644639.13
Variance11.143532
MonotonicityNot monotonic
2023-12-19T01:51:59.449274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 98
 
0.3%
19 79
 
0.2%
16 78
 
0.2%
17 77
 
0.2%
20 69
 
0.2%
17.5 48
 
0.1%
18.5 46
 
0.1%
21 45
 
0.1%
16.5 44
 
0.1%
22 43
 
0.1%
Other values (20708) 32705
98.1%
ValueCountFrequency (%)
8.395833333 1
< 0.1%
8.975609756 1
< 0.1%
9.3 1
< 0.1%
9.428571429 1
< 0.1%
9.653846154 1
< 0.1%
9.764705882 1
< 0.1%
9.876190476 1
< 0.1%
10 1
< 0.1%
10.16666667 1
< 0.1%
10.3 1
< 0.1%
ValueCountFrequency (%)
63.83950617 1
< 0.1%
59.86764706 1
< 0.1%
41.2 2
< 0.1%
39.60055866 1
< 0.1%
37.25 1
< 0.1%
37.01754386 1
< 0.1%
37 1
< 0.1%
36.98550725 1
< 0.1%
36.01470588 1
< 0.1%
35.98532495 1
< 0.1%

temperature_fahrenheit
Real number (ℝ)

Distinct11844
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.297958
Minimum0
Maximum171.825
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:51:59.643213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97.143348
Q197.95
median98.3
Q398.723256
95-th percentile99.585714
Maximum171.825
Range171.825
Interquartile range (IQR)0.77325581

Descriptive statistics

Standard deviation2.1718916
Coefficient of variation (CV)0.022094982
Kurtosis717.22602
Mean98.297958
Median Absolute Deviation (MAD)0.38333333
Skewness-12.707039
Sum3276467.5
Variance4.717113
MonotonicityNot monotonic
2023-12-19T01:51:59.863666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.2 297
 
0.9%
98.4 270
 
0.8%
98.3 255
 
0.8%
98.1 253
 
0.8%
98 233
 
0.7%
97.9 203
 
0.6%
98.6 192
 
0.6%
98.5 185
 
0.6%
97.8 153
 
0.5%
98.7 153
 
0.5%
Other values (11834) 31138
93.4%
ValueCountFrequency (%)
0 1
< 0.1%
7.4 1
< 0.1%
33.25 1
< 0.1%
33.5 1
< 0.1%
35.1125 1
< 0.1%
35.16666667 1
< 0.1%
35.225 1
< 0.1%
35.3 1
< 0.1%
35.8 1
< 0.1%
36 1
< 0.1%
ValueCountFrequency (%)
171.825 1
< 0.1%
164.3923077 1
< 0.1%
161.8285714 1
< 0.1%
157.4933333 1
< 0.1%
153.64375 1
< 0.1%
152.0125 1
< 0.1%
147.7222222 1
< 0.1%
145.2157895 1
< 0.1%
133.1384615 1
< 0.1%
125.8733333 1
< 0.1%

height_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.77103
Minimum0
Maximum292
Zeros31
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:52:00.123265image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile152
Q1163
median170
Q3178
95-th percentile185
Maximum292
Range292
Interquartile range (IQR)15

Descriptive statistics

Standard deviation14.194742
Coefficient of variation (CV)0.084106505
Kurtosis35.977298
Mean168.77103
Median Absolute Deviation (MAD)8
Skewness-3.7801345
Sum5625476
Variance201.49069
MonotonicityNot monotonic
2023-12-19T01:52:00.363640image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 3120
 
9.4%
173 2794
 
8.4%
168 2625
 
7.9%
170 2518
 
7.6%
165 2517
 
7.6%
163 2348
 
7.0%
175 2272
 
6.8%
183 2269
 
6.8%
157 1998
 
6.0%
160 1842
 
5.5%
Other values (193) 9029
27.1%
ValueCountFrequency (%)
0 31
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
13 8
 
< 0.1%
14 2
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
51 1
 
< 0.1%
ValueCountFrequency (%)
292 1
< 0.1%
284 1
< 0.1%
264 1
< 0.1%
249 1
< 0.1%
244 1
< 0.1%
241 2
< 0.1%
231 1
< 0.1%
226 2
< 0.1%
218 1
< 0.1%
215 1
< 0.1%

admission_weight_lbs
Real number (ℝ)

Distinct3621
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.4095
Minimum0
Maximum924.2
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:52:00.555917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile112.2
Q1147
median174.7
Q3208.1
95-th percentile270.6
Maximum924.2
Range924.2
Interquartile range (IQR)61.1

Descriptive statistics

Standard deviation52.442057
Coefficient of variation (CV)0.28908109
Kurtosis8.9499189
Mean181.4095
Median Absolute Deviation (MAD)30.1
Skewness1.6502129
Sum6046741.5
Variance2750.1693
MonotonicityNot monotonic
2023-12-19T01:52:00.733253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 404
 
1.2%
154 395
 
1.2%
165 323
 
1.0%
169.4 276
 
0.8%
198 274
 
0.8%
149.6 259
 
0.8%
132 256
 
0.8%
220 234
 
0.7%
171.6 219
 
0.7%
187 211
 
0.6%
Other values (3611) 30481
91.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.2 10
< 0.1%
14.3 1
 
< 0.1%
52.5 1
 
< 0.1%
52.8 1
 
< 0.1%
53.875 1
 
< 0.1%
54.1 1
 
< 0.1%
54.13333333 2
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
924.2 1
< 0.1%
918.5 1
< 0.1%
796.4 1
< 0.1%
792 1
< 0.1%
786.28 1
< 0.1%
705.5714286 1
< 0.1%
705.1 1
< 0.1%
633.2333333 1
< 0.1%
626.9 1
< 0.1%
605 1
< 0.1%
Distinct17718
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.699995
Minimum41.5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:52:00.898675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum41.5
5-th percentile93.839863
Q195.796714
median96.904762
Q397.903226
95-th percentile99.131433
Maximum100
Range58.5
Interquartile range (IQR)2.1065123

Descriptive statistics

Standard deviation1.9592427
Coefficient of variation (CV)0.020261042
Kurtosis69.005561
Mean96.699995
Median Absolute Deviation (MAD)1.0536348
Skewness-4.5001724
Sum3223204.2
Variance3.838632
MonotonicityNot monotonic
2023-12-19T01:52:01.506232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 141
 
0.4%
98 136
 
0.4%
96 110
 
0.3%
97.5 72
 
0.2%
99 72
 
0.2%
95 65
 
0.2%
96.5 65
 
0.2%
98.5 55
 
0.2%
97.25 54
 
0.2%
96.66666667 53
 
0.2%
Other values (17708) 32509
97.5%
ValueCountFrequency (%)
41.5 1
< 0.1%
47.2 1
< 0.1%
54.44 1
< 0.1%
57.85714286 1
< 0.1%
61.36363636 1
< 0.1%
62.27659574 1
< 0.1%
67 1
< 0.1%
68.1 1
< 0.1%
68.25 1
< 0.1%
69 1
< 0.1%
ValueCountFrequency (%)
100 39
0.1%
99.99065421 1
 
< 0.1%
99.98412698 1
 
< 0.1%
99.98076923 1
 
< 0.1%
99.97435897 1
 
< 0.1%
99.96969697 2
 
< 0.1%
99.96774194 1
 
< 0.1%
99.96428571 1
 
< 0.1%
99.96363636 1
 
< 0.1%
99.96153846 1
 
< 0.1%

pH_dipstick
Real number (ℝ)

MISSING 

Distinct201
Distinct (%)1.4%
Missing18615
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean5.9153486
Minimum5
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.8 KiB
2023-12-19T01:52:01.693478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15.5
median6
Q36.3333333
95-th percentile7
Maximum9
Range4
Interquartile range (IQR)0.83333333

Descriptive statistics

Standard deviation0.68927327
Coefficient of variation (CV)0.11652285
Kurtosis1.0007768
Mean5.9153486
Median Absolute Deviation (MAD)0.5
Skewness0.82966827
Sum87056.186
Variance0.47509763
MonotonicityNot monotonic
2023-12-19T01:52:01.906275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 3190
 
9.6%
5.5 2512
 
7.5%
5 2399
 
7.2%
6.5 1582
 
4.7%
7 772
 
2.3%
5.75 652
 
2.0%
6.25 424
 
1.3%
5.25 312
 
0.9%
7.5 254
 
0.8%
5.833333333 171
 
0.5%
Other values (191) 2449
 
7.3%
(Missing) 18615
55.8%
ValueCountFrequency (%)
5 2399
7.2%
5.055555556 1
 
< 0.1%
5.0625 2
 
< 0.1%
5.071428571 7
 
< 0.1%
5.083333333 7
 
< 0.1%
5.1 12
 
< 0.1%
5.125 24
 
0.1%
5.142857143 2
 
< 0.1%
5.153846154 1
 
< 0.1%
5.166666667 79
 
0.2%
ValueCountFrequency (%)
9 12
 
< 0.1%
8.75 2
 
< 0.1%
8.5 65
 
0.2%
8.416666667 2
 
< 0.1%
8.409090909 1
 
< 0.1%
8.25 4
 
< 0.1%
8.230769231 1
 
< 0.1%
8.083333333 1
 
< 0.1%
8.00862069 1
 
< 0.1%
8 165
0.5%

Interactions

2023-12-19T01:51:50.723720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:18.494532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:20.888235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:23.282179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:25.667789image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:28.181404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:30.901215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:33.143684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:35.681980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:38.120822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:40.887514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:43.183561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:45.563383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:47.894513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:50.888481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:18.643606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:21.063442image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:23.593673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:28.378109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:33.305243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:35.850904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:38.275016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:41.043457image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:43.353326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:45.723766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:51.083797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:18.798505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:21.198572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:31.256313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:33.525447image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:36.027231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:38.463938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:41.183556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:43.563620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:41.517975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:43.873773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:29.434862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:32.093817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:34.429549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:36.913744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:39.611197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-12-19T01:51:42.733683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:45.093520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:47.443691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:50.137510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:52.997864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:20.525253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:22.905767image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:25.313819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:27.783451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:30.285611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:32.835849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:35.293830image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:37.774849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:40.493688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:42.864987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:45.223392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:47.603677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:50.323598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:53.137188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:20.704856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:23.080533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:25.500456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:27.980415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:30.469574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:32.981461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:35.487868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:37.960025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:40.666440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:43.023387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:45.413671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:47.741315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-19T01:51:50.513460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-12-19T01:52:02.086512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
admission_typeadmission_weight_lbsageethnicityfirst_careunitgcs_eye_openinggcs_motor_responsegcs_verbal_responsegenderheart_rate_meanheight_cmhospital_expire_flagicu_losnon_invasive_blood_pressure_diastolicnon_invasive_blood_pressure_meano2_saturation_pulseoxymetry2pH_dipstickrespiratory_ratetemperature_fahrenheit
admission_type1.0000.0550.0220.0950.201-0.071-0.092-0.0540.058-0.0050.0190.1360.020-0.039-0.039-0.0270.0300.0080.012
admission_weight_lbs0.0551.000-0.1870.0370.035-0.043-0.045-0.0050.249-0.0050.4590.0510.0130.0870.084-0.169-0.0740.0280.097
age0.022-0.1871.0000.0710.084-0.019-0.034-0.0360.104-0.175-0.1690.1060.015-0.341-0.238-0.061-0.1720.067-0.221
ethnicity0.0950.0370.0711.0000.0650.0570.0550.0750.083-0.0450.0730.087-0.035-0.118-0.115-0.162-0.073-0.031-0.075
first_careunit0.2010.0350.0840.0651.0000.0610.198-0.0840.1110.120-0.0510.1870.1840.1970.2230.0000.1440.0730.226
gcs_eye_opening-0.071-0.043-0.0190.0570.0611.0000.8540.8230.065-0.060-0.0330.469-0.2870.0650.045-0.202-0.016-0.072-0.210
gcs_motor_response-0.092-0.045-0.0340.0550.1980.8541.0000.7980.076-0.069-0.0390.441-0.2590.0880.080-0.1910.002-0.086-0.162
gcs_verbal_response-0.054-0.005-0.0360.075-0.0840.8230.7981.0000.063-0.0990.0050.440-0.4190.0450.016-0.231-0.047-0.144-0.286
gender0.0580.2490.1040.0830.1110.0650.0760.0631.000-0.0430.6890.032-0.0240.1420.110-0.023-0.024-0.0280.024
heart_rate_mean-0.005-0.005-0.175-0.0450.120-0.060-0.069-0.099-0.0431.000-0.0260.1200.1400.1890.083-0.067-0.0030.3140.180
height_cm0.0190.459-0.1690.073-0.051-0.033-0.0390.0050.689-0.0261.0000.033-0.0180.1580.123-0.032-0.019-0.0400.039
hospital_expire_flag0.1360.0510.1060.0870.1870.4690.4410.4400.0320.1200.0331.0000.170-0.111-0.132-0.092-0.0700.183-0.004
icu_los0.0200.0130.015-0.0350.184-0.287-0.259-0.419-0.0240.140-0.0180.1701.000-0.0000.017-0.0160.0720.2440.223
non_invasive_blood_pressure_diastolic-0.0390.087-0.341-0.1180.1970.0650.0880.0450.1420.1890.158-0.111-0.0001.0000.923-0.0550.2290.0210.138
non_invasive_blood_pressure_mean-0.0390.084-0.238-0.1150.2230.0450.0800.0160.1100.0830.123-0.1320.0170.9231.000-0.0500.2330.0100.165
o2_saturation_pulseoxymetry2-0.027-0.169-0.061-0.1620.000-0.202-0.191-0.231-0.023-0.067-0.032-0.092-0.016-0.055-0.0501.0000.024-0.1980.010
pH_dipstick0.030-0.074-0.172-0.0730.144-0.0160.002-0.047-0.024-0.003-0.019-0.0700.0720.2290.2330.0241.000-0.0120.157
respiratory_rate0.0080.0280.067-0.0310.073-0.072-0.086-0.144-0.0280.314-0.0400.1830.2440.0210.010-0.198-0.0121.0000.192
temperature_fahrenheit0.0120.097-0.221-0.0750.226-0.210-0.162-0.2860.0240.1800.039-0.0040.2230.1380.1650.0100.1570.1921.000

Missing values

2023-12-19T01:51:53.403588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-19T01:51:53.873689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-19T01:51:54.300534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agegenderethnicityadmission_typefirst_careuniticu_loshospital_expire_flagheart_rate_meangcs_eye_openinggcs_verbal_responsegcs_motor_responsenon_invasive_blood_pressure_diastolicnon_invasive_blood_pressure_meanrespiratory_ratetemperature_fahrenheitheight_cmadmission_weight_lbso2_saturation_pulseoxymetry2pH_dipstick
558FWHITEEW EMER.Trauma SICU (TSICU)0.106192068.083333NaNNaNNaN85.333333101.83333316.25000098.800000152.0156.20000099.300000NaN
5656MWHITEEW EMER.Coronary Care Unit (CCU)0.440868083.666667NaNNaNNaN83.15384692.76923115.86666796.250000178.0152.70000097.785714NaN
5783FWHITEEW EMER.Coronary Care Unit (CCU)0.227558072.666667NaNNaNNaN53.40000068.00000024.50000097.500000147.0116.60000096.666667NaN
12870MWHITEEW EMER.Cardiac Vascular Intensive Care Unit (CVICU)0.325185184.571429NaNNaNNaNNaNNaN16.00000091.400000174.0136.50000089.400000NaN
14482FBLACK/AFRICAN AMERICANEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)0.977824063.8421054.0NaN6.053.38888967.42105318.52631697.750000157.0193.60000096.8500007.0
15140MWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)1.027164084.238095NaNNaNNaN84.47619096.55000020.25000098.428571160.0173.60000097.190476NaN
15354FWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)0.772049085.789474NaNNaNNaN59.44444468.66666718.94444497.780000160.0150.83333395.833333NaN
15623FBLACK/CARIBBEAN ISLANDEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)1.470938075.025000NaNNaNNaN61.29729773.29729726.35000098.900000160.0204.20000098.538462NaN
15877FWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)0.1782750100.608696NaNNaNNaN66.30000088.40000018.82608797.000000168.0152.90000097.739130NaN
16251FWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)0.091250076.500000NaNNaNNaN80.90909192.45454518.07142998.600000163.0169.40000098.071429NaN
agegenderethnicityadmission_typefirst_careuniticu_loshospital_expire_flagheart_rate_meangcs_eye_openinggcs_verbal_responsegcs_motor_responsenon_invasive_blood_pressure_diastolicnon_invasive_blood_pressure_meanrespiratory_ratetemperature_fahrenheitheight_cmadmission_weight_lbso2_saturation_pulseoxymetry2pH_dipstick
6899123FWHITEURGENTCardiac Vascular Intensive Care Unit (CVICU)4.972199068.2681162.9583332.04.87500058.66000069.84000019.81560398.700000152.0128.797.0281695.833333
6899366MWHITEURGENTCardiac Vascular Intensive Care Unit (CVICU)0.712326075.8000001.7500002.02.25000067.40000086.80000014.90000098.260000168.0144.598.200000NaN
6899469MUNABLE TO OBTAINURGENTCardiac Vascular Intensive Care Unit (CVICU)1.083171097.2058823.0000002.05.00000057.33333369.58333316.55882499.075000175.0196.093.843750NaN
6899537MWHITEEW EMER.Cardiac Vascular Intensive Care Unit (CVICU)1.240324090.3750001.7500002.02.25000063.50000073.25000020.38709798.257143170.0235.498.500000NaN
6899727MWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)1.137106091.2857143.2500002.06.00000077.42307792.38461517.071429100.312500180.0311.396.857143NaN
6899891MASIANEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)6.006076081.8142862.9428572.05.25714354.43884970.07857118.32394498.363889155.0151.899.321918NaN
6899980MWHITEURGENTMedical/Surgical Intensive Care Unit (MICU/SICU)3.129086076.5726503.0000002.05.17647153.23584971.77358518.31192798.056000175.0193.499.267241NaN
6900291FBLACK/AFRICAN AMERICANEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)1.451169073.2941182.8461542.05.46153860.47058875.05882417.38235397.416667168.0184.498.9705886.500000
6900356FWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)12.1009840100.2448283.7796612.04.88135659.42857170.78048815.24137998.353247160.0121.096.8793106.166667
6900464FWHITEEW EMER.Medical/Surgical Intensive Care Unit (MICU/SICU)4.1442130107.1040003.4000002.05.40000065.44000088.82000021.03968398.364000163.0132.097.566667NaN